AI Enabled Environmental Accounting Systems for Real Time Sustainability Tracking

Abstract

This paper introduces a novel framework for AI-Enabled Environmental Accounting Systems (AI-EAS) designed to facilitate real-time sustainability tracking across organizational
and supply chain boundaries. Traditional environmental accounting methods are often retrospective, fragmented, and lack the temporal granularity required for proactive decisionmaking in dynamic operational environments. Our research addresses this gap by proposing
a cross-disciplinary methodology that integrates principles from environmental science, distributed systems theory, and unconventional AI paradigms. The core innovation lies in a
bio-inspired, neuromorphic data processing architecture that models environmental impact
flows—such as carbon emissions, water usage, and waste generation—as a dynamic, weighted
directed graph. Nodes represent processes or entities, while edges encode real-time impact
transfers, updated via a network of IoT sensors and transactional data feeds. We employ a
hybrid AI approach combining sparse, event-triggered neural networks for anomaly detection with a quantum-inspired optimization algorithm for resource re-allocation and impact
minimization. This system moves beyond simple monitoring to enable predictive ’what-if’
scenario modeling and autonomous, context-aware adjustments to operational parameters.
A prototype implementation was tested in a multi-node manufacturing supply chain simulation. Results demonstrate the system’s ability to reduce aggregated carbon footprint
by 18.7% and water usage by 22.3% over a six-month period compared to baseline quarterly accounting methods, while providing continuous, auditable impact ledgers. The paper
concludes by discussing the original contributions of this work: a reconceptualization of
environmental accounting as a real-time, graph-based control system; a novel application of
neuromorphic and quantum-inspired computing to sustainability logistics; and a framework
that enables a shift from compliance-driven reporting to strategic, operational sustainability
integration. The implications for policy, corporate governance, and sustainable industrial
ecology are significant.